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NVIDIA NCP-ADS Exam Overview:
| Certification Vendor: | NVIDIA |
| Exam Name: | NVIDIA-Certified Professional: Accelerated Data Science |
| Exam Number: | NCP-ADS |
| Exam Format: | Multiple-choice, Scenario-based multiple-choice |
| Related Certifications: | NVIDIA-Certified Associate: Accelerated Data Science (NCA-ADS) |
| Certificate Validity Period: | 2 years |
| Available Languages: | Chinese, English |
| Real Exam Qty: | 60-70 |
| Exam Duration: | 120 minutes |
| Exam Price: | 1580 CNY (~$200 USD) |
| Recommended Training: | Fundamentals of Accelerated Data Science Accelerating End-to-End Data Science Workflows (DLI) |
| Exam Registration: | NVIDIA Training & Certification Portal NVIDIA Certification Support |
| Sample Questions: | NVIDIA NCP-ADS Sample Questions |
| Exam Way: | Proctored exam (online or authorized test center depending on region) |
| Pre Condition: | 2–3 years of experience in accelerated data science, machine learning, and GPU computing |
| Official Syllabus URL: | https://www.nvidia.cn/training/certification/accelerated-data-science-professional/ |
NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| MLOps | 19% | - Deployment and Monitoring
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Machine Learning | 15% | - Model Development and Optimization
|
| GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. You are working with a large dataset containing millions of high-resolution images for a deep learning project. The dataset needs to be processed efficiently on a GPU before training a model.
Which NVIDIA technology is best suited for preprocessing, augmenting, and efficiently loading the dataset into memory?
A) NVIDIA Triton Inference Server to preprocess the dataset before model training.
B) NVIDIA RAPIDS cuDF to transform image data into tabular format for analysis.
C) NVIDIA Nsight Compute to optimize image dataset processing.
D) NVIDIA DALI (Data Loading Library) to accelerate data loading and preprocessing on the GPU.
2. Which NVIDIA technology is specifically designed for accelerating deep learning workloads in the cloud?
A) TensorRT
B) NVIDIA A100
C) NVIDIA Tesla
D) NVIDIA Jetson
3. You are deploying an NVIDIA GPU-accelerated machine learning model in a Docker container and want to ensure that your application can leverage the GPU efficiently.
What is the best way to manage CUDA dependencies and avoid compatibility issues inside your Docker container?
A) Use a base Ubuntu image and install TensorFlow, PyTorch, and CUDA using pip install inside the container.
B) Use NVIDIA's official Docker images from NVIDIA GPU Cloud (NGC), which come with pre-installed CUDA and AI frameworks.
C) Manually install CUDA and cuDNN inside the container by downloading them from NVIDIA's website and setting environment variables.
D) Disable GPU acceleration in Docker and force computations on the CPU to avoid CUDA compatibility issues.
4. Which of the following is the best approach for performing benchmarking and optimizing GPU- accelerated workflows for MLOps using Nvidia technologies?
A) Use Nvidia's nvprof tool to profile GPU resource usage and identify bottlenecks, then adjust the batch size to optimize throughput.
B) Use Nvidia's nsight tools to benchmark only the model training phase and ignore the inference phase, as training is the primary bottleneck.
C) Rely exclusively on the nvidia-smi tool for monitoring GPU utilization and memory usage across multiple GPUs without making any other performance adjustments.
D) Use TensorRT to optimize deep learning models by converting them into highly optimized inference engines, allowing faster execution with lower latency.
5. You are training a machine learning model using scikit-learn-like API on a dataset with millions of samples and thousands of features. You need to optimize both training time and inference speed using NVIDIA technologies.
Which solution is the most appropriate?
A) Use NVIDIA Triton Inference Server to train the model efficiently on a single GPU.
B) Use NVIDIA Magnum IO to optimize machine learning model parameters on the GPU.
C) Use NVIDIA RAPIDS cuML for GPU-accelerated machine learning model training.
D) Use NVIDIA Modulus to accelerate machine learning training and feature selection.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: C |
By Neil
